Radiologists are medical doctors who are capable of interpreting images such as those obtained by conventional radiology, ultra-sonography (US), computed tomography (CT), and magnetic resonance imaging (MRI). As these imaging techniques have rapidly evolved technically, more and more anatomical details are available to be assessed non-invasively. For practicing radiologists, it can be difficult to be aware of all possible imaging presentations of all possible diseases. It is estimated that typically a clinical radiologist seeks diagnosis or anatomy assistance on 5-10% of cases daily. The problem is that referring to reference books, web searches, or colleagues interrupts work flow and reduces productivity. Although all information needed by the radiologist to make the correct diagnosis is usually available “somewhere”, the radiologist faces three problems with regard to obtaining this information as follows.
First, it may be difficult and/or time consuming to find the information required. Typically, a relevant imaging library contains hundreds of books and journals. It is not possible for an individual to catalogue or remember the precise content of all these books and journals. Internet searches are not necessarily more efficient: usually a query generates a large number of matches; finding the best match necessarily takes time and effort.
Secondly, the information may be fragmented, e.g. there may be basic information about a specific disease in a textbook, while for more detailed information dedicated Internet searches or other books may be required. Thus, even if the user knows where to find the information, finding the complete information he needs may not be straightforward.
Finally, most information is not presented in an optimal way. In clinical practice, the radiologist is confronted with specific morphologic patterns of disease at specific locations. As an example, he may see a “ring-enhancing focal lesion” (pattern) in the “brain” (location). In our hypothetical example, the radiologist would be interested to find information about brain and “ring-enhancing focal lesion”, with a description of different (common and uncommon) diseases that can cause this structural abnormality. He would further like to read a discussion of the relative likelihood of these diseases, means to differentiate between these diseases, and further information about each disease. Unfortunately, most radiology textbooks and other sources only provide a systematic overview of diseases according to location. For example, a radiological textbook focusing on liver diseases will describe the most important diseases one by one. In order to find out to what disease best matches a certain morphologic pattern, the radiologist first needs to have an a priori knowledge about which diseases could cause that pattern, and, secondly, has to find where exactly these diseases are discussed in the book (or other medium), and whether or not the description of imaging findings in this reference text indeed corresponds to his “case”. Such a search may be quite straightforward in some cases while it may be quite time-consuming and frustrating in others. Also, even the most recently developed databases on the Internet are organized by location, not by location and pattern.
Furthermore, information in most radiology textbooks and other references is not structured according to the work flow of the radiologist and finding practical information is difficult. FIG. 4 illustrates the steps presently required when a radiologist confronted with an abnormality at a certain location/sublocation and with a particular morphologic pattern wants to identify the corresponding disease(s). For difficult cases, he has to find an appropriate textbook to identify the diseases that may be present with a pattern similar to the one he has identified. During this process, the radiologist creates a list of possible diagnoses (Diagnosis 1 to n), from which the diseases that do not result in the appropriate pattern are eliminated. This list can further be refined by matching the clinical status of his particular patient (e.g., male or female) with the patient-related information provided for the different diseases (e.g. Disease No 1 tends to occur in women). It is clear that this is a quite inefficient process.